Starbucks Capstone Challenge¶

Introduction¶

This data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Some users might not receive any offer during certain weeks.

Not all users receive the same offer, and that is the challenge to solve with this data set.

Your task is to combine transaction, demographic and offer data to determine which demographic groups respond best to which offer type. This data set is a simplified version of the real Starbucks app because the underlying simulator only has one product whereas Starbucks actually sells dozens of products.

Every offer has a validity period before the offer expires. As an example, a BOGO offer might be valid for only 5 days. You'll see in the data set that informational offers have a validity period even though these ads are merely providing information about a product; for example, if an informational offer has 7 days of validity, you can assume the customer is feeling the influence of the offer for 7 days after receiving the advertisement.

You'll be given transactional data showing user purchases made on the app including the timestamp of purchase and the amount of money spent on a purchase. This transactional data also has a record for each offer that a user receives as well as a record for when a user actually views the offer. There are also records for when a user completes an offer.

Keep in mind as well that someone using the app might make a purchase through the app without having received an offer or seen an offer.

Example¶

To give an example, a user could receive a discount offer buy 10 dollars get 2 off on Monday. The offer is valid for 10 days from receipt. If the customer accumulates at least 10 dollars in purchases during the validity period, the customer completes the offer.

However, there are a few things to watch out for in this data set. Customers do not opt into the offers that they receive; in other words, a user can receive an offer, never actually view the offer, and still complete the offer. For example, a user might receive the "buy 10 dollars get 2 dollars off offer", but the user never opens the offer during the 10 day validity period. The customer spends 15 dollars during those ten days. There will be an offer completion record in the data set; however, the customer was not influenced by the offer because the customer never viewed the offer.

Cleaning¶

This makes data cleaning especially important and tricky.

You'll also want to take into account that some demographic groups will make purchases even if they don't receive an offer. From a business perspective, if a customer is going to make a 10 dollar purchase without an offer anyway, you wouldn't want to send a buy 10 dollars get 2 dollars off offer. You'll want to try to assess what a certain demographic group will buy when not receiving any offers.

Final Advice¶

Because this is a capstone project, you are free to analyze the data any way you see fit. For example, you could build a machine learning model that predicts how much someone will spend based on demographics and offer type. Or you could build a model that predicts whether or not someone will respond to an offer. Or, you don't need to build a machine learning model at all. You could develop a set of heuristics that determine what offer you should send to each customer (i.e., 75 percent of women customers who were 35 years old responded to offer A vs 40 percent from the same demographic to offer B, so send offer A).

Data Sets¶

The data is contained in three files:

portfolio.json - containing offer ids and meta data about each offer (duration, type, etc.)
profile.json - demographic data for each customer
transcript.json - records for transactions, offers received, offers viewed, and offers completed

Here is the schema and explanation of each variable in the files:

portfolio.json

id (string) - offer id
offer_type (string) - type of offer ie BOGO, discount, informational
difficulty (int) - minimum required spend to complete an offer
reward (int) - reward given for completing an offer
duration (int) - time for offer to be open, in days
channels (list of strings)

profile.json

age (int) - age of the customer
became_member_on (int) - date when customer created an app account
gender (str) - gender of the customer (note some entries contain 'O' for other rather than M or F)
id (str) - customer id
income (float) - customer's income

transcript.json

event (str) - record description (ie transaction, offer received, offer viewed, etc.)
person (str) - customer id
time (int) - time in hours since start of test. The data begins at time t=0
value - (dict of strings) - either an offer id or transaction amount depending on the record

Note: If you are using the workspace, you will need to go to the terminal and run the command conda update pandas before reading in the files. This is because the version of pandas in the workspace cannot read in the transcript.json file correctly, but the newest version of pandas can. You can access the termnal from the orange icon in the top left of this notebook.

You can see how to access the terminal and how the install works using the two images below. First you need to access the terminal:

Then you will want to run the above command:

Finally, when you enter back into the notebook (use the jupyter icon again), you should be able to run the below cell without any errors.

# import libraries here; add more as necessary
import numpy as np
import pandas as pd
import math
import json
import pickle
from datetime import datetime as dt
from IPython.display import display, HTML
import plotly
import matplotlib.pyplot as plt
import seaborn as sns
import re
# magic word for producing visualizations in notebook
%matplotlib inline
from sklearn import preprocessing as p
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.metrics import classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline, FeatureUnion

from sklearn.ensemble import AdaBoostClassifier
from sklearn import svm

import sys
np.set_printoptions(threshold=sys.maxsize)
print(sys.version)

# Set figure width to 12 and height to 9
fig_size = plt.rcParams["figure.figsize"]
fig_size[0] = 15
fig_size[1] = 10
plt.rcParams["figure.figsize"] = fig_size
pd.set_option('display.max_columns', 500)
import chart_studio as py
import plotly.graph_objects as go
import plotly.io as pio
import plotly.express as px
from plotly.subplots import make_subplots

3.7.3 (default, Mar 27 2019, 22:11:17) 
[GCC 7.3.0]

Load Data:¶

# read in the json files
portfolio = pd.read_json('data/portfolio.json', orient='records', lines=True)
profile = pd.read_json('data/profile.json', orient='records', lines=True)
transcript = pd.read_json('data/transcript.json', orient='records', lines=True)

portfolio

profile

transcript

Data Exploration:¶

# since any offer just started does not have any effect on the user therefore on our analysis and its outcome,
# we will take any transcript item with time = 0 from the transcript dataframe.
transcript.shape[0] - transcript[transcript['time']!=0].shape[0]

15561

profile.describe()

transcript.describe()

Feature Engineering:¶

def get_days(item_time_in_hours):
    item_days = int(round(item_time_in_hours / 24))
    return item_days

def extract_key_value(item_value):
    value_key = list(item_value)[0]
    item_amount = ''
    item_offer_id = ''
    if value_key == 'amount':
        item_amount = item_value[value_key]
    if value_key == 'offer id':
        item_offer_id = item_value[value_key]
    if value_key == 'offer_id':
        item_offer_id = item_value[value_key]
    return pd.Series([item_amount, item_offer_id])

def extract_event(item_event):
    offer_received = 0
    offer_viewed = 0
    offer_completed = 0
    transaction = 0
    if item_event == 'offer received':
        offer_received = 1
    if item_event == 'offer viewed':
        offer_viewed = 1
    if item_event == 'offer completed':
        offer_completed = 1
    if item_event == 'transaction':
        transaction = 1
    return pd.Series([offer_received, offer_viewed, offer_completed, transaction])

def clean_transcript(transcript_df):
    transcript_cleaned_df = transcript_df.copy()
    transcript_cleaned_df = transcript_cleaned_df[transcript_cleaned_df['time']!=0]
    transcript_cleaned_df[['transaction_amount','offer_id']] = transcript_cleaned_df.apply(lambda x: extract_key_value(x['value']), axis=1)
    transcript_cleaned_df[['offer_status_received', 'offer_status_viewed','offer_status_completed', 'transaction']] = transcript_cleaned_df.apply(lambda x: extract_event(x['event']), axis=1)
    transcript_cleaned_df['offer_days'] = transcript_cleaned_df.apply(lambda x: get_days(x['time']), axis=1)
    transcript_cleaned_df['user_id'] = transcript_cleaned_df['person']
    transcript_cleaned_df.drop(['event','time','value','person'], axis=1, inplace=True)
    transcript_cleaned_df = transcript_cleaned_df[['user_id','offer_id','transaction_amount','offer_days', 'offer_status_received','offer_status_viewed','offer_status_completed']]

    return transcript_cleaned_df

transcript_df = clean_transcript(transcript)

transcript_df

portfolio

def get_channels(item_channels):
    web = 0
    email = 0
    mobile = 0
    social = 0
    for i in item_channels:
        if i == 'web':
            web = 1
        if i == 'email':
            email = 1
        if i == 'mobile':
            mobile = 1
        if i == 'social':
            social = 1
    return  pd.Series([web, email, mobile, social])

def get_offer_type(offer_type):
    bogo = 0
    discount = 0
    informational = 0
    if offer_type == 'bogo':
        bogo = 1
    if offer_type == 'discount':
        discount = 1
    if offer_type == 'informational':
        informational = 1
    return  pd.Series([bogo, discount, informational])

def clean_portfolio(portfolio_df):
    portfolio_df_clean = portfolio_df.copy()
    offer_ids_list = list(pd.get_dummies(portfolio_df_clean['id'], prefix='offer_id_'))
    offer_ids_dummies = pd.get_dummies(portfolio_df_clean['id'], prefix='offer_id_')
    portfolio_df_clean[offer_ids_list] = offer_ids_dummies        
    portfolio_df_clean[['web', 'email', 'mobile', 'social']] = portfolio_df_clean.apply(lambda x: get_channels(x['channels']), axis=1)
    portfolio_df_clean[['bogo', 'discount', 'informational']] = portfolio_df_clean.apply(lambda x: get_offer_type(x['offer_type']), axis=1)
    portfolio_df_clean.drop(['channels'], axis=1, inplace=True)
    portfolio_df_clean.drop(['offer_type'], axis=1, inplace=True)
    portfolio_df_clean['offer_id'] = portfolio_df_clean['id']
    portfolio_df_clean['offer_duration'] = portfolio_df_clean['duration']
    portfolio_df_clean['offer_difficulty'] = portfolio_df_clean['difficulty']
    portfolio_df_clean['offer_reward'] = portfolio_df_clean['reward']
    portfolio_df_clean['offer_type_bogo'] = portfolio_df_clean['bogo']
    portfolio_df_clean['offer_type_discount'] = portfolio_df_clean['discount']
    portfolio_df_clean['offer_type_informational'] = portfolio_df_clean['informational']
    portfolio_df_clean['offer_channel_web'] = portfolio_df_clean['web']
    portfolio_df_clean['offer_channel_email'] = portfolio_df_clean['email']
    portfolio_df_clean['offer_channel_mobile'] = portfolio_df_clean['mobile']
    portfolio_df_clean['offer_channel_social'] = portfolio_df_clean['social']
    portfolio_df_clean = portfolio_df_clean[['offer_id','offer_duration','offer_difficulty','offer_reward','offer_type_bogo','offer_type_discount','offer_type_informational','offer_channel_web','offer_channel_email','offer_channel_mobile','offer_channel_social'] + offer_ids_list]
    return portfolio_df_clean

portfolio_df = clean_portfolio(portfolio)

portfolio_df

profile['age'].describe()

count    17000.000000
mean        62.531412
std         26.738580
min         18.000000
25%         45.000000
50%         58.000000
75%         73.000000
max        118.000000
Name: age, dtype: float64

profile['age'].hist()

<matplotlib.axes._subplots.AxesSubplot at 0x7fc91ba97780>

profile['income'].hist()

<matplotlib.axes._subplots.AxesSubplot at 0x7fc91ba5aa58>

gender_group = ['user_gender_F', 'user_gender_M', 'user_gender_O']
gender_group_info = ['Male','Female', 'Other']

age_group = ['user_age_group_1', 'user_age_group_2', 'user_age_group_3', 'user_age_group_4', 'user_age_group_5', 'user_age_group_6', 'user_age_group_7']
age_group_info = ['21 or younger','25 - 22', '35 - 26', '45 - 36', '55 - 46', '65 - 56', '66 or older']

loyality_group = ['user_loyality_group_1', 'user_loyality_group_2', 'user_loyality_group_3', 'user_loyality_group_4', 'user_loyality_group_5', 'user_loyality_group_6', 'user_loyality_group_7']
loyality_group_info = ['2013 or newer','2014 - 2013', '2015 - 2014', '2016 - 2015', '2017 - 2016', '2018 - 2017', '2018 or newer']

income_group = ['user_income_group_1', 'user_income_group_2', 'user_income_group_3', 'user_income_group_4', 'user_income_group_5', 'user_income_group_6', 'user_income_group_7']
income_group_info = ['35k or less','45k - 36k', '65k - 46k', '75k - 66k', '85k - 76k', '95k - 86k', '96k or more']

def age_group(age):
    if age <= 21:
        return 1
    if age <= 25:
        return 2
    if age <= 35:
        return 3
    if age <= 45:
        return 4
    if age <= 55:
        return 5
    if age <= 65:
        return 6
    if age > 65:
        return 7

def income_group(income):
    if income <= 35000:
        return 1
    if income <= 45000:
        return 2
    if income <= 65000:
        return 3
    if income <= 75000:
        return 4
    if income <= 85000:
        return 5
    if income <= 95000:
        return 6
    if income > 95000:
        return 7

def loyality_group(loyality):
    if loyality <= 20130000:
        return 1
    if loyality <= 20140000:
        return 2
    if loyality <= 20150000:
        return 3
    if loyality <= 20160000:
        return 4
    if loyality <= 20170000:
        return 5
    if loyality <= 20180000:
        return 6
    if loyality > 20180000:
        return 7

def clean_profile(profile_df):
    profile_df_cleaned = profile_df.copy()
    profile_df_cleaned = profile_df_cleaned.dropna()
    
    profile_df_cleaned[['user_gender_F','user_gender_M','user_gender_O']] = pd.get_dummies(profile_df_cleaned['gender'])
    
    profile_df_cleaned['user_id'] = profile_df_cleaned['id']   
    
    profile_df_cleaned['user_age_group'] = profile_df_cleaned['age'].apply(lambda x: age_group(x))
    profile_df_cleaned = pd.concat([profile_df_cleaned.drop('user_age_group', axis=1),pd.get_dummies(profile_df_cleaned['user_age_group'],prefix='user_age_group')], axis=1)
    
    profile_df_cleaned['user_loyality_group'] = profile_df_cleaned['became_member_on'].apply(lambda x: loyality_group(x))
    profile_df_cleaned = pd.concat([profile_df_cleaned.drop('user_loyality_group', axis=1),pd.get_dummies(profile_df_cleaned['user_loyality_group'],prefix='user_loyality_group')], axis=1)
    
    profile_df_cleaned['user_income_group'] = profile_df_cleaned['income'].apply(lambda x: income_group(x))
    profile_df_cleaned = pd.concat([profile_df_cleaned.drop('user_income_group', axis=1),pd.get_dummies(profile_df_cleaned['user_income_group'],prefix='user_income_group')], axis=1)
    profile_df_cleaned.drop(['id','age','became_member_on','gender','income'],axis=1,inplace=True)
    return profile_df_cleaned

profile_df = clean_profile(profile)

profile_df

profile['income'].describe()

count     14825.000000
mean      65404.991568
std       21598.299410
min       30000.000000
25%       49000.000000
50%       64000.000000
75%       80000.000000
max      120000.000000
Name: income, dtype: float64

profile['income'].hist()

<matplotlib.axes._subplots.AxesSubplot at 0x7fc91b9de400>

profile['age'].describe()

count    17000.000000
mean        62.531412
std         26.738580
min         18.000000
25%         45.000000
50%         58.000000
75%         73.000000
max        118.000000
Name: age, dtype: float64

profile['age'].hist()

<matplotlib.axes._subplots.AxesSubplot at 0x7fc91ba2d7b8>

transcript_df['transaction_amount'].describe()

count     290973
unique      5100
top             
freq      152653
Name: transaction_amount, dtype: object

transcript_protfolio_merged = pd.merge(transcript_df , portfolio_df, on='offer_id', how='outer')

transcript_protfolio_merged

transcript_protfolio_profile_merged = pd.merge(transcript_protfolio_merged, profile_df, on='user_id', how='outer')

transcript_protfolio_profile_merged

df = transcript_protfolio_profile_merged.copy()

df_calculated = df.copy()
df_calculated

def offer_success_calculation(df_calculated):

    df_calculated['offer_success'] = 0
    df_calculated['offer_amount'] = 0

    users = df_calculated['user_id'].unique()

    for user in users:
        
        df = df_calculated[df_calculated['user_id']==user]

        offers_list = df['offer_id'].unique()
        
        offers = []
        
        for i in offers_list:
            if (i != ''):
                if pd.notnull(i):
                    offers.append(i)



        for offer in offers:

            end = df[(df['offer_id']==offer)]['offer_days'].max()

            offer_idx = df[(df['offer_id']==offer)]

            received_day_index = pd.to_numeric(df[(df['offer_id']==offer)]['offer_status_received']).nlargest(1).index[0]
            received_day_value = pd.to_numeric(df[(df['offer_id']==offer)]['offer_status_received']).nlargest(1).iloc[0]

            viewed_day_index = pd.to_numeric(df[(df['offer_id']==offer)]['offer_status_viewed']).nlargest(1).index[0]
            viewed_day_value = pd.to_numeric(df[(df['offer_id']==offer)]['offer_status_viewed']).nlargest(1).iloc[0]

            completed_day_index = pd.to_numeric(df[(df['offer_id']==offer)]['offer_status_completed']).nlargest(1).index[0]
            completed_day_value = pd.to_numeric(df[(df['offer_id']==offer)]['offer_status_completed']).nlargest(1).iloc[0]

            start = df[(df['offer_id']==offer) & (df['offer_status_received']==1)]['offer_days'].max()
            
            duration = df[(df['offer_id']==offer)]['offer_duration'].iloc[0]
            
            diff = end - start

            received_day = offer_idx['offer_days'].loc[received_day_index]
            viewed_day = offer_idx['offer_days'].loc[viewed_day_index]
            completed_day = offer_idx['offer_days'].loc[completed_day_index]

            offer_amount = pd.to_numeric(df[(df['offer_days']==completed_day)]['transaction_amount']).max()

            summ = received_day_value + viewed_day_value + completed_day_value

            success=False

            if (diff <= duration) and (summ == 3) and (received_day <= viewed_day) and (viewed_day <= completed_day):

                success=True

            if (success==True):

                df_calculated.loc[offer_idx.index,'offer_success'] = 1

                if(offer_idx['offer_type_informational'].iloc[0] == 1):

                    df_calculated.loc[offer_idx.index,'offer_amount'] = 0

                if(offer_idx['offer_type_discount'].iloc[0] == 1):

                    df_calculated.loc[offer_idx.index,'offer_amount'] = offer_amount

                if(offer_idx['offer_type_bogo'].iloc[0] == 1):

                    df_calculated.loc[offer_idx.index,'offer_amount'] = offer_amount
    
    return df_calculated

df_calculated = offer_success_calculation(df_calculated)

df_calculated.to_csv('df_calculated.csv',index=False)

df_final = pd.read_csv('df_calculated.csv')

data = df_final.copy()

Data Analysis:¶

data = df_final.copy()
data.drop(['user_id','offer_id','transaction_amount'],axis=1,inplace=True)
data.dropna(inplace=True)

data.to_csv('data.csv',index=False)

data = pd.read_csv('data.csv')

success_data = data[data['offer_success']==1]

success_data

success_data.to_csv('success_data.csv')

offer_columns = ['offer_id__0b1e1539f2cc45b7b9fa7c272da2e1d7','offer_id__2298d6c36e964ae4a3e7e9706d1fb8c2','offer_id__2906b810c7d4411798c6938adc9daaa5','offer_id__3f207df678b143eea3cee63160fa8bed','offer_id__4d5c57ea9a6940dd891ad53e9dbe8da0','offer_id__5a8bc65990b245e5a138643cd4eb9837','offer_id__9b98b8c7a33c4b65b9aebfe6a799e6d9','offer_id__ae264e3637204a6fb9bb56bc8210ddfd','offer_id__f19421c1d4aa40978ebb69ca19b0e20d','offer_id__fafdcd668e3743c1bb461111dcafc2a4']
offer_columns

['offer_id__0b1e1539f2cc45b7b9fa7c272da2e1d7',
 'offer_id__2298d6c36e964ae4a3e7e9706d1fb8c2',
 'offer_id__2906b810c7d4411798c6938adc9daaa5',
 'offer_id__3f207df678b143eea3cee63160fa8bed',
 'offer_id__4d5c57ea9a6940dd891ad53e9dbe8da0',
 'offer_id__5a8bc65990b245e5a138643cd4eb9837',
 'offer_id__9b98b8c7a33c4b65b9aebfe6a799e6d9',
 'offer_id__ae264e3637204a6fb9bb56bc8210ddfd',
 'offer_id__f19421c1d4aa40978ebb69ca19b0e20d',
 'offer_id__fafdcd668e3743c1bb461111dcafc2a4']

success_data=pd.read_csv('success_data.csv').drop('Unnamed: 0',axis=1)
success_data

success_data[(success_data['offer_id__0b1e1539f2cc45b7b9fa7c272da2e1d7']==1)]

successful_offers = []
for i in offer_columns:
    successful_offers.append(success_data[success_data[i]==1])

offer_features = ['offer_success','offer_amount','offer_days','offer_status_received','offer_status_viewed','offer_status_completed','offer_duration','offer_difficulty','offer_reward','offer_type_bogo','offer_type_discount','offer_type_informational','offer_channel_web','offer_channel_email','offer_channel_mobile','offer_channel_social']
successful_offers_demographic = []
for df in successful_offers:
    successful_offers_demographic.append(df.drop(offer_columns+offer_features,axis=1))

successful_offers_demographic[0]

successful_offers_demographic_sums = []
for i in range(0,10): 
    successful_offers_demographic_sums.append(successful_offers_demographic[i].sum(axis = 0))
    successful_offers_demographic_sums[i] = successful_offers_demographic_sums[i].append(pd.Series({'success_count' :successful_offers_demographic[i].shape[0]}))
    successful_offers_demographic_sums[i] = successful_offers_demographic_sums[i].append(pd.Series({'offer_id' :offer_columns[i].split('_')[3]}))
    
successful_offers_demographic_sums[0]

user_gender_F                                        1559
user_gender_M                                        1668
user_gender_O                                          98
user_age_group_1                                       45
user_age_group_2                                       69
user_age_group_3                                      129
user_age_group_4                                      511
user_age_group_5                                      774
user_age_group_6                                      844
user_age_group_7                                      953
user_loyality_group_2                                  46
user_loyality_group_3                                 167
user_loyality_group_4                                 393
user_loyality_group_5                                1008
user_loyality_group_6                                1173
user_loyality_group_7                                 538
user_income_group_1                                    79
user_income_group_2                                   198
user_income_group_3                                  1102
user_income_group_4                                   623
user_income_group_5                                   539
user_income_group_6                                   476
user_income_group_7                                   308
success_count                                        3325
offer_id                 0b1e1539f2cc45b7b9fa7c272da2e1d7
dtype: object

successful_offers_demographic_sums[0]

user_gender_F                                        1559
user_gender_M                                        1668
user_gender_O                                          98
user_age_group_1                                       45
user_age_group_2                                       69
user_age_group_3                                      129
user_age_group_4                                      511
user_age_group_5                                      774
user_age_group_6                                      844
user_age_group_7                                      953
user_loyality_group_2                                  46
user_loyality_group_3                                 167
user_loyality_group_4                                 393
user_loyality_group_5                                1008
user_loyality_group_6                                1173
user_loyality_group_7                                 538
user_income_group_1                                    79
user_income_group_2                                   198
user_income_group_3                                  1102
user_income_group_4                                   623
user_income_group_5                                   539
user_income_group_6                                   476
user_income_group_7                                   308
success_count                                        3325
offer_id                 0b1e1539f2cc45b7b9fa7c272da2e1d7
dtype: object

successful_offers_demographic_sums_df = pd.DataFrame(successful_offers_demographic_sums)
successful_offers_demographic_sums_df

df = successful_offers_demographic_sums_df
df

df = successful_offers_demographic_sums_df

gender_group = ['user_gender_F', 'user_gender_M', 'user_gender_O']
gender_group

['user_gender_F', 'user_gender_M', 'user_gender_O']

gender_group_info = ['Male','Female', 'Other']
gender_group_info

['Male', 'Female', 'Other']

age_group = ['user_age_group_1', 'user_age_group_2', 'user_age_group_3', 'user_age_group_4', 'user_age_group_5', 'user_age_group_6', 'user_age_group_7']
age_group

['user_age_group_1',
 'user_age_group_2',
 'user_age_group_3',
 'user_age_group_4',
 'user_age_group_5',
 'user_age_group_6',
 'user_age_group_7']

age_group_info = ['21 or younger','25 - 22', '35 - 26', '45 - 36', '55 - 46', '65 - 56', '66 or older']
age_group_info

['21 or younger',
 '25 - 22',
 '35 - 26',
 '45 - 36',
 '55 - 46',
 '65 - 56',
 '66 or older']

loyality_group = ['user_loyality_group_1', 'user_loyality_group_2', 'user_loyality_group_3', 'user_loyality_group_4', 'user_loyality_group_5', 'user_loyality_group_6', 'user_loyality_group_7']
loyality_group

['user_loyality_group_1',
 'user_loyality_group_2',
 'user_loyality_group_3',
 'user_loyality_group_4',
 'user_loyality_group_5',
 'user_loyality_group_6',
 'user_loyality_group_7']

loyality_group_info = ['2013 or newer','2014 - 2013', '2015 - 2014', '2016 - 2015', '2017 - 2016', '2018 - 2017', '2018 or newer']
loyality_group_info

['2013 or newer',
 '2014 - 2013',
 '2015 - 2014',
 '2016 - 2015',
 '2017 - 2016',
 '2018 - 2017',
 '2018 or newer']

income_group = ['user_income_group_1', 'user_income_group_2', 'user_income_group_3', 'user_income_group_4', 'user_income_group_5', 'user_income_group_6', 'user_income_group_7']
income_group

['user_income_group_1',
 'user_income_group_2',
 'user_income_group_3',
 'user_income_group_4',
 'user_income_group_5',
 'user_income_group_6',
 'user_income_group_7']

income_group_info = ['35k or less','45k - 36k', '65k - 46k', '75k - 66k', '85k - 76k', '95k - 86k', '96k or more']
income_group_info

['35k or less',
 '45k - 36k',
 '65k - 46k',
 '75k - 66k',
 '85k - 76k',
 '95k - 86k',
 '96k or more']

# Create figure with secondary y-axis
from plotly.subplots import make_subplots

fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add traces
for (feature, info) in zip(gender_group, gender_group_info):
    fig.add_trace(
        go.Scatter(x=list(df.offer_id), y=df[feature], name=info),

    )

    # Add figure title
fig.update_layout(
    title_text="User Gender For Each Offer"
)
# Set x-axis title
fig.update_xaxes(title_text="Offer")
# Set y-axes titles
fig.update_yaxes(title_text="Count Of User Group Per Successful Offer")

fig.show()

# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add traces
for (feature, info) in zip(age_group, age_group_info):
    fig.add_trace(
        go.Scatter(x=list(df.offer_id), y=df[feature], name=info),

    )
# Add figure title
fig.update_layout(
    title_text="User Age Group For Each Offer"
)
# Set x-axis title
fig.update_xaxes(title_text="Offer")
# Set y-axes titles
fig.update_yaxes(title_text="Count Of User Group Per Successful Offer")

fig.show()

# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add traces
for (feature, info) in zip(income_group, income_group_info):
    fig.add_trace(
        go.Scatter(x=list(df.offer_id), y=df[feature], name=info),

    )

    # Add figure title
fig.update_layout(
    title_text="User Income Group For Each Offer"
)
# Set x-axis title
fig.update_xaxes(title_text="Offer")
# Set y-axes titles
fig.update_yaxes(title_text="Number Of User Group Per Successful Offer")

fig.show()

# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])
loyality_group.pop(0) # no users in group 1
loyality_group_info.pop(0) # no users in group 1
# Add traces
for (feature, info) in zip(loyality_group, loyality_group_info):
    fig.add_trace(
        go.Scatter(x=list(df.offer_id), y=df[feature], name=info),

    )

    # Add figure title
fig.update_layout(
    title_text="User Loyality Group For Each Offer"
)
# Set x-axis title
fig.update_xaxes(title_text="Offer")
# Set y-axes titles
fig.update_yaxes(title_text="Number Of User Group Per Successful Offer")

fig.show()

#Mostly user_gender_M, user_age_group_7, user_income_group_3, user_loyality_group_6

Conclusion¶

After studying all the data and the graphs generated after thorough investigation and transformations of the givine data, I can say the following:
All Offers are succesfful for this user demographic:
Mostly Female, 65 years of age or more, Earns between 45000 and 65000 and Joined recently in 2018 - 2017 period.

	age	became_member_on	gender	id	income
0	118	20170212	None	68be06ca386d4c31939f3a4f0e3dd783	NaN
1	55	20170715	F	0610b486422d4921ae7d2bf64640c50b	112000.0
2	118	20180712	None	38fe809add3b4fcf9315a9694bb96ff5	NaN
3	75	20170509	F	78afa995795e4d85b5d9ceeca43f5fef	100000.0
4	118	20170804	None	a03223e636434f42ac4c3df47e8bac43	NaN
5	68	20180426	M	e2127556f4f64592b11af22de27a7932	70000.0
6	118	20170925	None	8ec6ce2a7e7949b1bf142def7d0e0586	NaN
7	118	20171002	None	68617ca6246f4fbc85e91a2a49552598	NaN
8	65	20180209	M	389bc3fa690240e798340f5a15918d5c	53000.0
9	118	20161122	None	8974fc5686fe429db53ddde067b88302	NaN
10	118	20170824	None	c4863c7985cf408faee930f111475da3	NaN
11	118	20150919	None	148adfcaa27d485b82f323aaaad036bd	NaN
12	58	20171111	M	2eeac8d8feae4a8cad5a6af0499a211d	51000.0
13	61	20170911	F	aa4862eba776480b8bb9c68455b8c2e1	57000.0
14	26	20140213	M	e12aeaf2d47d42479ea1c4ac3d8286c6	46000.0
15	62	20160211	F	31dda685af34476cad5bc968bdb01c53	71000.0
16	49	20141113	M	62cf5e10845442329191fc246e7bcea3	52000.0
17	118	20170801	None	744d603ef08c4f33af5a61c8c7628d1c	NaN
18	57	20171231	M	6445de3b47274c759400cd68131d91b4	42000.0
19	61	20180501	F	a448667f336b42c9a66fc5ffd5d73772	40000.0
20	40	20160504	F	440cf1fd7580490c971d8c651ed962af	71000.0
21	64	20170909	M	4b0da7e80e5945209a1fdddfe813dbe0	100000.0
22	78	20170616	F	c27e0d6ab72c455a8bb66d980963de60	71000.0
23	118	20170907	None	2b826eba31074a059d63b0ae8f50b7d5	NaN
24	42	20130811	M	f806632c011441378d4646567f357a21	69000.0
25	56	20180428	F	d058f73bf8674a26a95227db098147b1	88000.0
26	118	20170330	None	65aba5c617294649aeb624da249e1ee5	NaN
27	33	20170926	F	c6c9884912c645429f3333f912b55f44	52000.0
28	46	20170911	F	7429a044884842d6862f516c38b1156f	59000.0
29	59	20150121	M	ebe7ef46ea6f4963a7dd49f501b26779	41000.0
...	...	...	...	...	...
16970	67	20151107	M	a2e6029e17b6466187a40b66e333a73e	94000.0
16971	52	20180522	F	6e71b66784844d1ab8376ac8ab096d4b	75000.0
16972	44	20170119	F	e2fd53ed790240c586b3188f23542cca	51000.0
16973	30	20151012	M	08eb126ad33f447ca3ad076482445c05	57000.0
16974	59	20160113	F	f88e91a11d8f405b9a47ad3741415b83	71000.0
16975	61	20171231	F	1b4df4b48bf64727a4c445909aea1251	81000.0
16976	39	20160921	F	122c0c2a3c2540848f8c3fdc37e97639	54000.0
16977	118	20160609	None	eb7dd979f4644052a5c401a01f129132	NaN
16978	29	20170220	F	54bead4179c44b71acc4e8398181d183	58000.0
16979	63	20130922	F	a00058cf10334a308c68e7631c529907	52000.0
16980	118	20160901	None	5c686d09ca4d475a8f750f2ba07e0440	NaN
16981	84	20160629	M	1966fa40d2f84620b2b1b9b64f8e0209	93000.0
16982	118	20160415	None	d9ca82f550ac4ee58b6299cf1e5c824a	NaN
16983	72	20150404	F	ff737c250d2343729ade04c4f6eb1001	65000.0
16984	75	20160716	F	392d23b2c958493197f647bedfe4f418	78000.0
16985	26	20180619	M	97ee6e7a12fe4064b260fa48bdd0330f	55000.0
16986	59	20170731	F	bf3069b178fe40d789dd027901bc406b	63000.0
16987	57	20160709	M	76ddbd6576844afe811f1a3c0fbb5bec	40000.0
16988	64	20180104	M	f653cf2d8bba42d0a53c2937ee2e5893	51000.0
16989	118	20180305	None	ca45ee1883624304bac1e4c8a114f045	NaN
16990	70	20160310	F	79edb810789c447e8d212a324b44cc16	39000.0
16991	118	20160116	None	a9a20fa8b5504360beb4e7c8712f8306	NaN
16992	21	20170917	F	400d0536e8794cbb855b0d882d67cbda	72000.0
16993	60	20180505	M	cb23b66c56f64b109d673d5e56574529	113000.0
16994	118	20151211	None	c02b10e8752c4d8e9b73f918558531f7	NaN
16995	45	20180604	F	6d5f3a774f3d4714ab0c092238f3a1d7	54000.0
16996	61	20180713	M	2cb4f97358b841b9a9773a7aa05a9d77	72000.0
16997	49	20170126	M	01d26f638c274aa0b965d24cefe3183f	73000.0
16998	83	20160307	F	9dc1421481194dcd9400aec7c9ae6366	50000.0
16999	62	20170722	F	e4052622e5ba45a8b96b59aba68cf068	82000.0

	event	person	time	value
0	offer received	78afa995795e4d85b5d9ceeca43f5fef	0	{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}
1	offer received	a03223e636434f42ac4c3df47e8bac43	0	{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}
2	offer received	e2127556f4f64592b11af22de27a7932	0	{'offer id': '2906b810c7d4411798c6938adc9daaa5'}
3	offer received	8ec6ce2a7e7949b1bf142def7d0e0586	0	{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}
4	offer received	68617ca6246f4fbc85e91a2a49552598	0	{'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}
5	offer received	389bc3fa690240e798340f5a15918d5c	0	{'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}
6	offer received	c4863c7985cf408faee930f111475da3	0	{'offer id': '2298d6c36e964ae4a3e7e9706d1fb8c2'}
7	offer received	2eeac8d8feae4a8cad5a6af0499a211d	0	{'offer id': '3f207df678b143eea3cee63160fa8bed'}
8	offer received	aa4862eba776480b8bb9c68455b8c2e1	0	{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}
9	offer received	31dda685af34476cad5bc968bdb01c53	0	{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}
10	offer received	744d603ef08c4f33af5a61c8c7628d1c	0	{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}
11	offer received	3d02345581554e81b7b289ab5e288078	0	{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}
12	offer received	4b0da7e80e5945209a1fdddfe813dbe0	0	{'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}
13	offer received	c27e0d6ab72c455a8bb66d980963de60	0	{'offer id': '3f207df678b143eea3cee63160fa8bed'}
14	offer received	d53717f5400c4e84affdaeda9dd926b3	0	{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}
15	offer received	f806632c011441378d4646567f357a21	0	{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}
16	offer received	d058f73bf8674a26a95227db098147b1	0	{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}
17	offer received	65aba5c617294649aeb624da249e1ee5	0	{'offer id': '2906b810c7d4411798c6938adc9daaa5'}
18	offer received	ebe7ef46ea6f4963a7dd49f501b26779	0	{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}
19	offer received	1e9420836d554513ab90eba98552d0a9	0	{'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}
20	offer received	868317b9be554cb18e50bc68484749a2	0	{'offer id': '2906b810c7d4411798c6938adc9daaa5'}
21	offer received	f082d80f0aac47a99173ba8ef8fc1909	0	{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}
22	offer received	102e9454054946fda62242d2e176fdce	0	{'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}
23	offer received	4beeb3ed64dd4898b0edf2f6b67426d3	0	{'offer id': '2906b810c7d4411798c6938adc9daaa5'}
24	offer received	9f30b375d7bd4c62a884ffe7034e09ee	0	{'offer id': '2298d6c36e964ae4a3e7e9706d1fb8c2'}
25	offer received	25c906289d154b66bf579693f89481c9	0	{'offer id': '2906b810c7d4411798c6938adc9daaa5'}
26	offer received	6e014185620b49bd98749f728747572f	0	{'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}
27	offer received	02c083884c7d45b39cc68e1314fec56c	0	{'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}
28	offer received	c0d210398dee4a0895b24444a5fcd1d2	0	{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}
29	offer received	8be4463721e14d7fa600686bf8c8b2ed	0	{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}
...	...	...	...	...
306504	transaction	8524d450673b4c24869b6c94380006de	714	{'amount': 4.89}
306505	transaction	b895c57e8cd047a8872ce02aa54759d6	714	{'amount': 4.48}
306506	offer completed	b895c57e8cd047a8872ce02aa54759d6	714	{'offer_id': 'fafdcd668e3743c1bb461111dcafc2a4...
306507	offer viewed	8dda575c2a1d44b9ac8e8b07b93d1f8e	714	{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}
306508	transaction	8431c16f8e1d440880db371a68f82dd0	714	{'amount': 1.19}
306509	offer completed	8431c16f8e1d440880db371a68f82dd0	714	{'offer_id': 'fafdcd668e3743c1bb461111dcafc2a4...
306510	transaction	ba620885e51c4b0ea64a4f61daad494f	714	{'amount': 14.31}
306511	transaction	a1a8f40407c444cc848468275308958a	714	{'amount': 2.37}
306512	transaction	8d80970192fa496f99d6b45c470a4b60	714	{'amount': 6.92}
306513	transaction	bde275066f3c4fa0bff3093e3b866a2c	714	{'amount': 12.73}
306514	transaction	f1e4fd36e5a0446f83861308bddf6945	714	{'amount': 8.2}
306515	transaction	0b64be3b241c4407a5c9a71781173829	714	{'amount': 2.6}
306516	transaction	86d03d35d7e0434b935e7743e83be3a0	714	{'amount': 9.2}
306517	transaction	3408fd05c781401f8442fb6dbaaea9c7	714	{'amount': 11.7}
306518	transaction	1593d617fac246ef8e50dbb0ffd77f5f	714	{'amount': 40.67}
306519	transaction	f1b31d07b5d84f69a2d5f1d07843989e	714	{'amount': 31.13}
306520	transaction	2ce987015ec0404a97ba333e8e814090	714	{'amount': 1.6400000000000001}
306521	transaction	2e33545f0a764d27b2ccff95fc8d72c4	714	{'amount': 17.35}
306522	transaction	d1c4500ace2e45e9a45d3cd2fccac8d8	714	{'amount': 4.42}
306523	transaction	b65affd9e07346a1906364a396950e3d	714	{'amount': 18.35}
306524	transaction	d613ca9c59dd42f497bdbf6178da54a7	714	{'amount': 25.14}
306525	transaction	eec70ab28af74a22a4aeb889c0317944	714	{'amount': 43.58}
306526	transaction	24f56b5e1849462093931b164eb803b5	714	{'amount': 22.64}
306527	offer completed	24f56b5e1849462093931b164eb803b5	714	{'offer_id': 'fafdcd668e3743c1bb461111dcafc2a4...
306528	transaction	5ca2620962114246ab218fc648eb3934	714	{'amount': 2.2}
306529	transaction	b3a1272bc9904337b331bf348c3e8c17	714	{'amount': 1.5899999999999999}
306530	transaction	68213b08d99a4ae1b0dcb72aebd9aa35	714	{'amount': 9.53}
306531	transaction	a00058cf10334a308c68e7631c529907	714	{'amount': 3.61}
306532	transaction	76ddbd6576844afe811f1a3c0fbb5bec	714	{'amount': 3.5300000000000002}
306533	transaction	c02b10e8752c4d8e9b73f918558531f7	714	{'amount': 4.05}

	user_id	offer_id	transaction_amount	offer_days	offer_status_received	offer_status_viewed	offer_status_completed
15561	78afa995795e4d85b5d9ceeca43f5fef	9b98b8c7a33c4b65b9aebfe6a799e6d9		0	0	1	0
15562	a03223e636434f42ac4c3df47e8bac43	0b1e1539f2cc45b7b9fa7c272da2e1d7		0	0	1	0
15563	102e9454054946fda62242d2e176fdce		19.88	0	0	0	0
15564	102e9454054946fda62242d2e176fdce	4d5c57ea9a6940dd891ad53e9dbe8da0		0	0	0	1
15565	02c083884c7d45b39cc68e1314fec56c		1.44	0	0	0	0
15566	be8a5d1981a2458d90b255ddc7e0d174		2.63	0	0	0	0
15567	7584948ea6d04f94b8798624706261c2	4d5c57ea9a6940dd891ad53e9dbe8da0		0	0	1	0
15568	a5a6ef2b78a04ddc9f374dd7c7f60bff	5a8bc65990b245e5a138643cd4eb9837		0	0	1	0
15569	8045834137dc47faa24f2c4547969f33	f19421c1d4aa40978ebb69ca19b0e20d		0	0	1	0
15570	571b739ada3041fb8fbd20f23ce034f7	4d5c57ea9a6940dd891ad53e9dbe8da0		0	0	1	0
15571	a9780228a1d04539afdee8b745e4be10	fafdcd668e3743c1bb461111dcafc2a4		0	0	1	0
15572	13033baa9d294c3c9165a00d71b72614	2906b810c7d4411798c6938adc9daaa5		0	0	1	0
15573	2cf5895bddf9467e878c6d51a622d5fb	f19421c1d4aa40978ebb69ca19b0e20d		0	0	1	0
15574	6747dda08bed46298c23e05b64820a3a		7.32	0	0	0	0
15575	f35b342b24734da18a830bb9329a4fbd	4d5c57ea9a6940dd891ad53e9dbe8da0		0	0	1	0
15576	37a97f6e09784a07bbb67bc6539f1a00	3f207df678b143eea3cee63160fa8bed		0	0	1	0
15577	37a97f6e09784a07bbb67bc6539f1a00		24.25	0	0	0	0
15578	fe97aa22dd3e48c8b143116a8403dd52	fafdcd668e3743c1bb461111dcafc2a4		0	0	1	0
15579	d7dc31e069124ef49e1bb937cb89f41c	4d5c57ea9a6940dd891ad53e9dbe8da0		0	0	1	0
15580	d250372c924747d8ac9a8fa88f584bc3	f19421c1d4aa40978ebb69ca19b0e20d		0	0	1	0
15581	d1a6fe4c241e4dbf8a4da6fd6c714ac5	9b98b8c7a33c4b65b9aebfe6a799e6d9		0	0	1	0
15582	ae47fbe72b274838a2093c3e91bc03dc	ae264e3637204a6fb9bb56bc8210ddfd		0	0	1	0
15583	e737ff591120415581ddbe817b8c82ca		23.27	0	0	0	0
15584	32189735a963491082e35dd4c3221690		21.22	0	0	0	0
15585	18174519037241e9b1ddcb627ca4dc41	5a8bc65990b245e5a138643cd4eb9837		0	0	1	0
15586	031387fa9cfd47e88f4e4b4b4cb71925	fafdcd668e3743c1bb461111dcafc2a4		0	0	1	0
15587	09e7e40eb89943458f4f79fe2e023dd6	fafdcd668e3743c1bb461111dcafc2a4		0	0	1	0
15588	c894971c9e414a358b84255199727636	2298d6c36e964ae4a3e7e9706d1fb8c2		0	0	1	0
15589	c894971c9e414a358b84255199727636		6.97	0	0	0	0
15590	00c6035df45840038a72766c6d27a0db	fafdcd668e3743c1bb461111dcafc2a4		0	0	1	0
...	...	...	...	...	...	...	...
306504	8524d450673b4c24869b6c94380006de		4.89	30	0	0	0
306505	b895c57e8cd047a8872ce02aa54759d6		4.48	30	0	0	0
306506	b895c57e8cd047a8872ce02aa54759d6	fafdcd668e3743c1bb461111dcafc2a4		30	0	0	1
306507	8dda575c2a1d44b9ac8e8b07b93d1f8e	0b1e1539f2cc45b7b9fa7c272da2e1d7		30	0	1	0
306508	8431c16f8e1d440880db371a68f82dd0		1.19	30	0	0	0
306509	8431c16f8e1d440880db371a68f82dd0	fafdcd668e3743c1bb461111dcafc2a4		30	0	0	1
306510	ba620885e51c4b0ea64a4f61daad494f		14.31	30	0	0	0
306511	a1a8f40407c444cc848468275308958a		2.37	30	0	0	0
306512	8d80970192fa496f99d6b45c470a4b60		6.92	30	0	0	0
306513	bde275066f3c4fa0bff3093e3b866a2c		12.73	30	0	0	0
306514	f1e4fd36e5a0446f83861308bddf6945		8.2	30	0	0	0
306515	0b64be3b241c4407a5c9a71781173829		2.6	30	0	0	0
306516	86d03d35d7e0434b935e7743e83be3a0		9.2	30	0	0	0
306517	3408fd05c781401f8442fb6dbaaea9c7		11.7	30	0	0	0
306518	1593d617fac246ef8e50dbb0ffd77f5f		40.67	30	0	0	0
306519	f1b31d07b5d84f69a2d5f1d07843989e		31.13	30	0	0	0
306520	2ce987015ec0404a97ba333e8e814090		1.64	30	0	0	0
306521	2e33545f0a764d27b2ccff95fc8d72c4		17.35	30	0	0	0
306522	d1c4500ace2e45e9a45d3cd2fccac8d8		4.42	30	0	0	0
306523	b65affd9e07346a1906364a396950e3d		18.35	30	0	0	0
306524	d613ca9c59dd42f497bdbf6178da54a7		25.14	30	0	0	0
306525	eec70ab28af74a22a4aeb889c0317944		43.58	30	0	0	0
306526	24f56b5e1849462093931b164eb803b5		22.64	30	0	0	0
306527	24f56b5e1849462093931b164eb803b5	fafdcd668e3743c1bb461111dcafc2a4		30	0	0	1
306528	5ca2620962114246ab218fc648eb3934		2.2	30	0	0	0
306529	b3a1272bc9904337b331bf348c3e8c17		1.59	30	0	0	0
306530	68213b08d99a4ae1b0dcb72aebd9aa35		9.53	30	0	0	0
306531	a00058cf10334a308c68e7631c529907		3.61	30	0	0	0
306532	76ddbd6576844afe811f1a3c0fbb5bec		3.53	30	0	0	0
306533	c02b10e8752c4d8e9b73f918558531f7		4.05	30	0	0	0

	user_gender_F	user_gender_M	user_gender_O	user_id	user_age_group_1	user_age_group_2	user_age_group_3	user_age_group_4	user_age_group_5	user_age_group_6	user_age_group_7	user_loyality_group_2	user_loyality_group_3	user_loyality_group_4	user_loyality_group_5	user_loyality_group_6	user_loyality_group_7	user_income_group_1	user_income_group_2	user_income_group_3	user_income_group_4	user_income_group_5	user_income_group_6	user_income_group_7
1	1	0	0	0610b486422d4921ae7d2bf64640c50b	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1
3	1	0	0	78afa995795e4d85b5d9ceeca43f5fef	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1
5	0	1	0	e2127556f4f64592b11af22de27a7932	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0
8	0	1	0	389bc3fa690240e798340f5a15918d5c	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	1	0	0	0	0
12	0	1	0	2eeac8d8feae4a8cad5a6af0499a211d	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0
13	1	0	0	aa4862eba776480b8bb9c68455b8c2e1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0
14	0	1	0	e12aeaf2d47d42479ea1c4ac3d8286c6	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0
15	1	0	0	31dda685af34476cad5bc968bdb01c53	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0
16	0	1	0	62cf5e10845442329191fc246e7bcea3	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0
18	0	1	0	6445de3b47274c759400cd68131d91b4	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	0	0
19	1	0	0	a448667f336b42c9a66fc5ffd5d73772	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0
20	1	0	0	440cf1fd7580490c971d8c651ed962af	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0
21	0	1	0	4b0da7e80e5945209a1fdddfe813dbe0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1
22	1	0	0	c27e0d6ab72c455a8bb66d980963de60	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0
24	0	1	0	f806632c011441378d4646567f357a21	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0
25	1	0	0	d058f73bf8674a26a95227db098147b1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	0
27	1	0	0	c6c9884912c645429f3333f912b55f44	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0
28	1	0	0	7429a044884842d6862f516c38b1156f	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0
29	0	1	0	ebe7ef46ea6f4963a7dd49f501b26779	0	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0
30	0	1	0	5917d13f6fa04286abdf95f4d5cefb1e	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	1
31	0	0	1	d1ede868e29245ea91818a903fec04c6	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0
32	0	1	0	1e9420836d554513ab90eba98552d0a9	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0
33	1	0	0	868317b9be554cb18e50bc68484749a2	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0
34	0	1	0	f082d80f0aac47a99173ba8ef8fc1909	0	0	0	1	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0
35	1	0	0	102e9454054946fda62242d2e176fdce	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0
37	0	1	0	8dabe2142cdf4013a6f4786edf8ab00f	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	0	0
38	1	0	0	9f30b375d7bd4c62a884ffe7034e09ee	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0
40	0	1	0	6e014185620b49bd98749f728747572f	0	0	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0
41	1	0	0	02c083884c7d45b39cc68e1314fec56c	1	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0
42	0	1	0	c0d210398dee4a0895b24444a5fcd1d2	0	0	0	1	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
16963	1	0	0	8c3fbf6b89b248bb99b7a6ca628f401c	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0
16964	0	1	0	68213b08d99a4ae1b0dcb72aebd9aa35	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0
16965	0	1	0	e340cbf00a914f0c958577c0af36ed8a	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0
16966	0	1	0	10316535e185400fb093c371d62254e3	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0
16967	0	1	0	cec1cdc6b77e41abbdce8903c5522e7e	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0
16968	0	1	0	2c6f50c1c07a444896e2a4e31d724f92	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0
16970	0	1	0	a2e6029e17b6466187a40b66e333a73e	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	0
16971	1	0	0	6e71b66784844d1ab8376ac8ab096d4b	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0
16972	1	0	0	e2fd53ed790240c586b3188f23542cca	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0
16973	0	1	0	08eb126ad33f447ca3ad076482445c05	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0
16974	1	0	0	f88e91a11d8f405b9a47ad3741415b83	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0
16975	1	0	0	1b4df4b48bf64727a4c445909aea1251	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0
16976	1	0	0	122c0c2a3c2540848f8c3fdc37e97639	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0
16978	1	0	0	54bead4179c44b71acc4e8398181d183	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0
16979	1	0	0	a00058cf10334a308c68e7631c529907	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	1	0	0	0	0
16981	0	1	0	1966fa40d2f84620b2b1b9b64f8e0209	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0
16983	1	0	0	ff737c250d2343729ade04c4f6eb1001	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0
16984	1	0	0	392d23b2c958493197f647bedfe4f418	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	0
16985	0	1	0	97ee6e7a12fe4064b260fa48bdd0330f	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0
16986	1	0	0	bf3069b178fe40d789dd027901bc406b	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0
16987	0	1	0	76ddbd6576844afe811f1a3c0fbb5bec	0	0	0	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0
16988	0	1	0	f653cf2d8bba42d0a53c2937ee2e5893	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	1	0	0	0	0
16990	1	0	0	79edb810789c447e8d212a324b44cc16	0	0	0	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0
16992	1	0	0	400d0536e8794cbb855b0d882d67cbda	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0
16993	0	1	0	cb23b66c56f64b109d673d5e56574529	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	1
16995	1	0	0	6d5f3a774f3d4714ab0c092238f3a1d7	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0
16996	0	1	0	2cb4f97358b841b9a9773a7aa05a9d77	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0
16997	0	1	0	01d26f638c274aa0b965d24cefe3183f	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0
16998	1	0	0	9dc1421481194dcd9400aec7c9ae6366	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0
16999	1	0	0	e4052622e5ba45a8b96b59aba68cf068	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0

	user_gender_F	user_gender_M	user_gender_O	user_age_group_1	user_age_group_2	user_age_group_3	user_age_group_4	user_age_group_5	user_age_group_6	user_age_group_7	user_loyality_group_2	user_loyality_group_3	user_loyality_group_4	user_loyality_group_5	user_loyality_group_6	user_loyality_group_7	user_income_group_1	user_income_group_2	user_income_group_3	user_income_group_4	user_income_group_5	user_income_group_6	user_income_group_7	success_count	offer_id
0	1559.0	1668.0	98.0	45.0	69.0	129.0	511.0	774.0	844.0	953.0	46.0	167.0	393.0	1008.0	1173.0	538.0	79.0	198.0	1102.0	623.0	539.0	476.0	308.0	3325.0	0b1e1539f2cc45b7b9fa7c272da2e1d7
1	4561.0	5755.0	154.0	291.0	374.0	888.0	1505.0	2148.0	2322.0	2942.0	214.0	583.0	1254.0	2673.0	3858.0	1888.0	714.0	1208.0	3324.0	1918.0	1146.0	930.0	1230.0	10470.0	2298d6c36e964ae4a3e7e9706d1fb8c2
2	2333.0	2920.0	114.0	107.0	112.0	309.0	818.0	1166.0	1351.0	1504.0	130.0	294.0	724.0	1461.0	1860.0	898.0	256.0	391.0	1785.0	1024.0	759.0	635.0	517.0	5367.0	2906b810c7d4411798c6938adc9daaa5
3	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3f207df678b143eea3cee63160fa8bed
4	3806.0	3327.0	107.0	141.0	145.0	457.0	945.0	1694.0	1678.0	2180.0	93.0	219.0	961.0	1993.0	2984.0	990.0	261.0	552.0	1990.0	1476.0	953.0	828.0	1180.0	7240.0	4d5c57ea9a6940dd891ad53e9dbe8da0
5	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5a8bc65990b245e5a138643cd4eb9837
6	2462.0	2859.0	116.0	94.0	107.0	348.0	858.0	1313.0	1242.0	1475.0	120.0	227.0	809.0	1378.0	1943.0	960.0	255.0	365.0	1815.0	1024.0	738.0	648.0	592.0	5437.0	9b98b8c7a33c4b65b9aebfe6a799e6d9
7	3278.0	3312.0	119.0	150.0	152.0	473.0	985.0	1472.0	1655.0	1822.0	86.0	149.0	853.0	1878.0	2632.0	1111.0	316.0	579.0	2169.0	1205.0	902.0	722.0	816.0	6709.0	ae264e3637204a6fb9bb56bc8210ddfd
8	3935.0	4583.0	146.0	256.0	260.0	667.0	1071.0	1918.0	2044.0	2448.0	152.0	391.0	1052.0	2386.0	3276.0	1407.0	492.0	859.0	2785.0	1577.0	962.0	876.0	1113.0	8664.0	f19421c1d4aa40978ebb69ca19b0e20d
9	4764.0	5730.0	128.0	216.0	327.0	979.0	1404.0	2183.0	2617.0	2896.0	245.0	565.0	1510.0	2729.0	3666.0	1907.0	751.0	1138.0	3434.0	1857.0	1181.0	1036.0	1225.0	10622.0	fafdcd668e3743c1bb461111dcafc2a4

	channels	difficulty	duration	id	offer_type	reward
0	[email, mobile, social]	10	7	ae264e3637204a6fb9bb56bc8210ddfd	bogo	10
1	[web, email, mobile, social]	10	5	4d5c57ea9a6940dd891ad53e9dbe8da0	bogo	10
2	[web, email, mobile]	0	4	3f207df678b143eea3cee63160fa8bed	informational	0
3	[web, email, mobile]	5	7	9b98b8c7a33c4b65b9aebfe6a799e6d9	bogo	5
4	[web, email]	20	10	0b1e1539f2cc45b7b9fa7c272da2e1d7	discount	5
5	[web, email, mobile, social]	7	7	2298d6c36e964ae4a3e7e9706d1fb8c2	discount	3
6	[web, email, mobile, social]	10	10	fafdcd668e3743c1bb461111dcafc2a4	discount	2
7	[email, mobile, social]	0	3	5a8bc65990b245e5a138643cd4eb9837	informational	0
8	[web, email, mobile, social]	5	5	f19421c1d4aa40978ebb69ca19b0e20d	bogo	5
9	[web, email, mobile]	10	7	2906b810c7d4411798c6938adc9daaa5	discount	2

	age	became_member_on	income
count	17000.000000	1.700000e+04	14825.000000
mean	62.531412	2.016703e+07	65404.991568
std	26.738580	1.167750e+04	21598.299410
min	18.000000	2.013073e+07	30000.000000
25%	45.000000	2.016053e+07	49000.000000
50%	58.000000	2.017080e+07	64000.000000
75%	73.000000	2.017123e+07	80000.000000
max	118.000000	2.018073e+07	120000.000000

	time
count	306534.000000
mean	366.382940
std	200.326314
min	0.000000
25%	186.000000
50%	408.000000
75%	528.000000
max	714.000000

	offer_days	offer_status_received	offer_status_viewed	offer_status_completed	offer_duration	offer_difficulty	offer_reward	offer_type_bogo	offer_type_discount	offer_type_informational	offer_channel_web	offer_channel_email	offer_channel_mobile	offer_channel_social	offer_id__0b1e1539f2cc45b7b9fa7c272da2e1d7	offer_id__2298d6c36e964ae4a3e7e9706d1fb8c2	offer_id__2906b810c7d4411798c6938adc9daaa5	offer_id__3f207df678b143eea3cee63160fa8bed	offer_id__4d5c57ea9a6940dd891ad53e9dbe8da0	offer_id__5a8bc65990b245e5a138643cd4eb9837	offer_id__9b98b8c7a33c4b65b9aebfe6a799e6d9	offer_id__ae264e3637204a6fb9bb56bc8210ddfd	offer_id__f19421c1d4aa40978ebb69ca19b0e20d	offer_id__fafdcd668e3743c1bb461111dcafc2a4	user_gender_F	user_gender_M	user_gender_O	user_age_group_1	user_age_group_2	user_age_group_3	user_age_group_4	user_age_group_5	user_age_group_6	user_age_group_7	user_loyality_group_2	user_loyality_group_3	user_loyality_group_4	user_loyality_group_5	user_loyality_group_6	user_loyality_group_7	user_income_group_1	user_income_group_2	user_income_group_3	user_income_group_4	user_income_group_5	user_income_group_6	user_income_group_7	offer_success	offer_amount
7	17	1	0	0	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1	21.72
8	17	0	1	0	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1	21.72
9	21	0	0	1	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1	21.72
19	24	1	0	0	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1	15.90
20	24	0	1	0	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1	15.90
21	24	0	0	1	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1	15.90
28	24	1	0	0	5.0	5.0	5.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1	11.17
29	25	0	1	0	5.0	5.0	5.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1	11.17
30	26	0	0	1	5.0	5.0	5.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1	11.17
31	17	1	0	0	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1	10.74
32	18	0	1	0	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1	10.74
33	20	0	0	1	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1	10.74
36	21	1	0	0	10.0	20.0	5.0	0.0	1.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	713.12
37	22	0	1	0	10.0	20.0	5.0	0.0	1.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	713.12
38	23	0	0	1	10.0	20.0	5.0	0.0	1.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	713.12
39	24	1	0	0	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	37.10
40	25	0	1	0	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	37.10
41	28	0	0	1	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	37.10
44	7	1	0	0	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	33.49
45	7	0	1	0	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	33.49
46	11	0	0	1	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	33.49
49	14	1	0	0	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	22.31
50	14	0	1	0	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	22.31
51	16	0	0	1	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	22.31
55	14	1	0	0	7.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1	14.70
56	15	0	1	0	7.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1	14.70
57	16	0	0	1	7.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1	14.70
85	14	1	0	0	5.0	5.0	5.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1	21.62
86	14	0	1	0	5.0	5.0	5.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1	21.62
87	14	0	0	1	5.0	5.0	5.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1	21.62
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
134756	24	1	0	0	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1	23.07
134757	25	0	1	0	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1	23.07
134758	28	0	0	1	7.0	10.0	10.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1	23.07
134776	14	1	0	0	7.0	7.0	3.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1	31.18
134777	15	0	1	0	7.0	7.0	3.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1	31.18
134778	24	1	0	0	7.0	7.0	3.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1	31.18
134779	24	0	1	0	7.0	7.0	3.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1	31.18
134780	28	0	0	1	7.0	7.0	3.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1	31.18
134782	24	1	0	0	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1	18.92
134783	24	0	1	0	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1	18.92
134784	28	0	0	1	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1	18.92
134792	24	1	0	0	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1	27.26
134793	24	0	1	0	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1	27.26
134794	28	0	0	1	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1	27.26
134797	24	1	0	0	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1	16.18
134798	24	0	1	0	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1	16.18
134799	29	0	0	1	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1	16.18
134813	14	1	0	0	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1	27.11
134814	15	0	1	0	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1	27.11
134815	24	1	0	0	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1	27.11
134816	24	0	1	0	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1	27.11
134817	29	0	0	1	10.0	10.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1	27.11
134826	24	1	0	0	5.0	10.0	10.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1	25.50
134827	25	0	1	0	5.0	10.0	10.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1	25.50
134828	29	0	0	1	5.0	10.0	10.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1	25.50
134845	14	1	0	0	5.0	10.0	10.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	26.79
134846	14	0	1	0	5.0	10.0	10.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	26.79
134847	24	1	0	0	5.0	10.0	10.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	26.79
134848	25	0	1	0	5.0	10.0	10.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	26.79
134849	29	0	0	1	5.0	10.0	10.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1	26.79

	user_gender_F	user_gender_M	user_gender_O	user_age_group_1	user_age_group_2	user_age_group_3	user_age_group_4	user_age_group_5	user_age_group_6	user_age_group_7	user_loyality_group_2	user_loyality_group_3	user_loyality_group_4	user_loyality_group_5	user_loyality_group_6	user_loyality_group_7	user_income_group_1	user_income_group_2	user_income_group_3	user_income_group_4	user_income_group_5	user_income_group_6	user_income_group_7
12	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
13	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
14	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
48	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
49	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
50	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
57	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
58	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
59	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
72	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
73	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
74	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
99	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
100	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
101	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
146	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
147	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
148	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
149	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
150	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
154	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
155	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
156	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
163	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
164	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
165	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
169	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
170	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
171	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
184	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
34667	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
34668	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
34669	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
34688	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
34689	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
34690	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
34706	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
34707	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
34708	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
34718	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
34719	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
34720	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
34724	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
34725	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
34726	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
34748	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
34749	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
34750	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
34784	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
34785	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
34786	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
34795	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
34796	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
34797	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
34846	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
34847	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
34848	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
34865	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
34866	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
34867	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0